
\section{Introduction}
\label{sec:introduction}

\textbf{Transfer learning} is a term used in the field of machine learning and
psychology. In psychology it is the study of the dependency of human conduct,
learning, or performance on prior experience. It is the reason humans (and
animals) learn incredibly fast and efficient.

The way humans learn is focused on reusing \textbf{existing knowledge} whenever
possible. In order to reuse knowledge, patterns between domains have to be
discovered. Once these patterns are revealed the source domain (experience) and
target domain (learning task) can be linked. Linked domains enable us to
transfer our knowledge.

An \textbf{example}: John plays the guitar (source domain) and never saw a piano (target
domain) before. If we ask him to learn to play the piano, he will probably
discover soon that a piano is a musical instrument similar to his guitar. Guitar
strings (variables of sort a) correspond to piano keys (variables of sort b).
Finding a mapping between a and b is not very hard in this example. For many
music notes there exist a direct mapping between a and b. When John is aware of
this mapping he can play his guitar songs on the piano in no time, compared to
someone that has no musical background.

To most of us this example sounds rather trivial. It is obvious (to us) that a
mapping exists between the variables a and b. When learning, humans are
constantly looking for patterns and mappings between domains. The golden ratio
and the many phenomena that are Gaussian distributed are just examples of the
many correspondences that exist across all sorts of domains (anything in the
world can be a domain).

Incorporating transfer learning in \textbf{machine learning techniques} has an enormous
potential. What about classifying flowers with email spam filters? The 'only
thing' we have to do is find a mapping between emails and flowers. These
mappings can be direct (variable-to-variable) or indirect (by use of meta
features). Algorithms that find these mappings automatically are the main topic
of this paper.

% BENJAMIN

The domain of interest in this project is monitoring human activities with
\textbf{wireless sensor networks} (WSN in short). These consist of a number of sensors
that make some individual observation and send it to a central hub, which then
processes the data. For instance, a WSN to observe forest fires could consist
of a number of smoke and heat detectors; these can be used to detect, localize,
and observe the evolution of a fire.

This project uses the data sets from \cite{tim2008} and thus works with WSNs
installed in households (see fig.~\ref{fig:housesAB}, used for activity
recognition. Examples for activities to be detected are ``Leave house'', ``Take
shower'', ``Prepare Dinner'', ``Get Drink'', and so on.

\begin{figure}
\centering
\includegraphics[width=0.45\textwidth]{dario_01a_houses.png}
\caption{Two house layouts with sensors (red dots). Sensors have to be matched
in order to make transfer learning possible.}
\label{fig:housesAB}
\end{figure}

